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Record W2092055659 · doi:10.1213/ane.0b013e3181ae901c

Stroke Volume Calculation by Esophageal Doppler Integrates Velocity Over Time and Multiplies This “Area Under The Curve” by the Cross Sectional Area of the Aorta

2009· erratum· en· W2092055659 on OpenAlexaffabout
Thomas L. Archer, Duane J. Funk, Eugene Moretti, Tong J. Gan

Bibliographic record

VenueAnesthesia & Analgesia · 2009
Typeerratum
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversity of ManitobaResearch Manitoba
Fundersnot available
KeywordsStroke volumeMedicineStroke (engine)AortaNuclear medicineGeometryCardiologyMathematicsPhysicsEjection fraction

Abstract

fetched live from OpenAlex

To the Editor: In our recent review1 we wish to acknowledge an error that was astutely observed by Dr. Archer. In describing the calculation of stroke volume by means of esophageal Doppler, we state that the area under the curve of the velocity-time graph “is computed mathematically as the integral of the derivative of velocity over time (dV/dt) from T0 to T1 (where T0 is the start of aortic blood flow and T1 is the end of flow).” This is not accurate. The area under the curve for the velocity-time graph should be described as the integral of the velocity curve over time, not the integral of its derivative. The area under this curve is the distance traveled by blood during systole, also called the stroke distance, measured in cm. Stroke volume is then obtained by multiplying stroke distance by the cross sectional area of aorta (cm2) to obtain stroke volume (cm3). We thank Dr. Archer for pointing out this error and apologize for any confusion this may have caused. Thomas L. Archer, MD, MBA University of California San Diego, California [email protected] Duane J. Funk, MD, FRCP(C) University of Manitoba Winnipeg, Manitoba, Canada Eugene Moretti, MD Tong J. Gan, MD, FRCA Department of Anesthesia Duke University Medical Center Durham, North Carolina

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2009
Admission routes2
Has abstractyes

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